Polynomial-fit based reference line smoothing method for high speed planning of autonomous driving vehicles

ABSTRACT

According to various embodiments, systems and methods for smoothing a reference line for an autonomous driving vehicle are described. In an exemplary method, a raw reference line can be generated from a high definition map and routing results, and can be truncated based on a predetermined formula. The truncated raw reference line can include a number of points, each point representing a position of an autonomous driving vehicle in a global coordinate system. The number of points can be converted to points in a local coordinate system, where a polynomial curve that best fits the points are generated. The polynomial curve can subsequently be used to generate a new vertical coordinate for a horizontal coordinate of each of the number of points. The new vertical coordinates and their corresponding horizontal coordinates can be converted back to the global coordinate system. The polynomial curve can be used to derive a heading, a kappa, and a dkappa for each point in the global coordinate system.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a U.S. National Phase Application under 35U.S.C. § 371 of International Application No. PCT/CN2018/123936, filedDec. 26, 2018, entitled “A POLYNOMIAL-FIT BASED REFERENCE LINE SMOOTHINGMETHOD FOR HIGH SPEED PLANNING OF AUTONOMOUS DRIVING VEHICLES,” which isincorporated by reference herein by its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to a reference line smoothing method in an autonomous drivingvehicle.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors and high-definitionmaps, allowing the vehicle to travel with minimal human interaction orin some cases without any passengers.

An autonomous driving vehicle (ADV) relies on various modules to plantrajectories. Typically, a reference line can be generated in a form ofa topographic map for each planned trajectory, and can represent anideal route or path without any interference from others such as othervehicles, obstacles, or traffic condition. The reference line can besmoothed using various techniques for comfortable driving.

However, when the ADV travels at a high speed, there would be a higherrequirement for path point curvatures and their derivatives in order tomaintain a particular centripetal acceleration. Existing smoothingtechniques generally cannot meet the requirements in a high-speedenvironment.

SUMMARY

In an aspect of the disclosure, embodiments of the disclosure provide acomputer-implemented method for operating an autonomous driving vehicle,the method including: transferring a reference line generated for anautonomous driving vehicle (ADV) from a global coordinate system to alocal coordinate system, the reference line including a plurality ofdiscrete points; determining, using a polynomial curve that best fitsthe plurality of points, a new vertical coordinate for a horizontalcoordinate of each point on the reference line; transferring pointsrepresented by each new vertical coordinate and its correspondinghorizontal coordinate back to the global coordinate system; anddetermining, using the polynomial curve, a heading direction, a kappa,and a dkappa for each transferred point.

In another aspect of the disclosure, embodiments of the disclosureprovide a non-transitory machine-readable medium having instructionsstored therein, which when executed by a processor, cause the processorto perform operations, the operations including: transferring areference line generated for an autonomous driving vehicle (ADV), from aglobal coordinate system to a local coordinate system, the referenceline including a plurality of discrete points; determining, using apolynomial curve that best matches the plurality of points, a newvertical coordinate for a horizontal coordinate of each point on thereference line; transferring points represented by each new verticalcoordinate and its corresponding horizontal coordinate back to theglobal coordinate system; and determining, using the polynomial curve, aheading direction, a kappa, and a dkappa for each transferred point.

In another aspect of the disclosure, embodiments of the disclosureprovide a data processing system, the system includes a processor; and amemory coupled to the processor to store instructions, which whenexecuted by the processor, causing the processor to perform operations,the operations includes: transferring a reference line generated for anautonomous driving vehicle (ADV) from a global coordinate system to alocal coordinate system, the reference line including a plurality ofdiscrete points, determining, using a polynomial curve that best matchesthe plurality of points, a new vertical coordinate for a horizontalcoordinate of each point on the reference line, transferring pointsrepresented by each new vertical coordinate and its correspondinghorizontal coordinate back to the global coordinate system, anddetermining, using the polynomial curve, a heading direction, a kappa,and a dkappa for each transferred point.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of a perceptionand planning system used with an autonomous vehicle according to oneembodiment.

FIG. 4 illustrates an environment where a polynomial-fit based referenceline smoothing component operates in an ADV in accordance with anembodiment.

FIG. 5 further illustrates a polynomial-fit based reference linesmoothing component in accordance with an embodiment.

FIGS. 6A-6E graphically illustrate a polynomial-fit based reference linesmoothing method in accordance with an embodiment.

FIG. 7 is a flow diagram illustrating an example process of smoothing areference line in accordance with an embodiment.

FIG. 8 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

According to various embodiments, systems and methods for smoothing areference line using a polynomial-fit curve are described. In anexemplary method, a raw reference line can be generated from a highdefinition map and routing results, and can be truncated based on apredetermined formula. The truncated raw reference line can include anumber of points, each point representing a position of an autonomousdriving vehicle in a global coordinate system. The number of points canbe converted to points in a local coordinate system, where a polynomialcurve that best fits the points are generated.

The polynomial curve can subsequently be used to generate a new verticalcoordinate for a horizontal coordinate of each of the number of points.The new vertical coordinates and their corresponding horizontalcoordinates can be converted back to the global coordinate system. Thepolynomial curve can be used to derive a heading, a kappa, and a dkappafor each point in the global coordinate system. The transferred pointsand the heading, the kappa and the dkappa for each point are used tocreate a new reference line for the ADV.

In an embodiment, the heading, the kappa, and the dkappa of eachtransferred points can be computed respectively using the first orderderivative, the second order derivative, and the third derivative of thepolynomial curve. Since the polynomial curve is a continuous, smooth,and unbroken line, its first order derivative, the second orderderivative, and the third derivative are also smooth, thereby making theheadings, the kappas, and the dkappas of the transferred points smoothand continuous.

Therefore, a reference line generated using the above-described approachwould enable smoother and more comfortable driving. Embodimentsdescribed in this disclosure are particularly useful in an ADV that aredesigned to travel in a high-speed road conditions.

In an embodiment, the raw reference line can be generated for a planningphase that may include multiple driving/planning cycles. Each point onthe raw reference line can represent a position of the ADV is to be at aparticular point of time during the planning cycle.

In an embodiment, the polynomial curve can be in the format ofY=C_3*X{circumflex over ( )}3+C_2*X{circumflex over( )}2+C_1*X{circumflex over ( )}1+C_0*X{circumflex over ( )}0. The abovedescribed reference line smoothing method can be used when the ADVtravels at a speed exceeding a predetermined value. When the ADV travelsat a speed at or below the predetermined value, the ADV can beautomatically switched to another process implementing a differentreference line smoothing method.

Autonomous Driving Vehicle

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) servers, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113,infotainment system 114, and sensor system 115. Autonomous vehicle 101may further include certain common components included in ordinaryvehicles, such as, an engine, wheels, steering wheel, transmission,etc., which may be controlled by vehicle control system 111 and/orperception and planning system 110 using a variety of communicationsignals and/or commands, such as, for example, acceleration signals orcommands, deceleration signals or commands, steering signals orcommands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn control the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyboard, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes, including algorithms to generate reference linesusing polynomial curve fitting techniques. Algorithms 124 can then beuploaded on ADVs to be utilized during autonomous driving in real-time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, and routing module 307.

Some or all of modules 301-307 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-307may be integrated together as an integrated module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration, traffic light signals, arelative position of another vehicle, a pedestrian, a building,crosswalk, or other traffic related signs (e.g., stop signs, yieldsigns), etc., for example, in a form of an object. The laneconfiguration includes information describing a lane or lanes, such as,for example, a shape of the lane (e.g., straight or curvature), a widthof the lane, how many lanes in a road, one-way or two-way lane, mergingor splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follows thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal routes in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle), using areference line provided by routing module 307 as a basis. That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, steering commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as driving cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or driving cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to affect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this disclosure. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

Polynomial-Fit Based Reference Line Smoothing

FIG. 4 illustrates an environment where a polynomial-fit based referenceline smoothing component operates in an ADV in accordance with anembodiment. As shown in FIG. 4, the routing module 307 in a perceptionand planning system 110 can include a generic reference line smoothingcomponent 401 and a polynomial-fit based reference line smoothingcomponent 403. Which reference line smoothing component to use can bebased on a number of factors, including an expected speed of the ADV ina particular road segment.

For example, if the ADV gets on a high way with a speed limit of 70miles per hour, the ADV can expect to travel at a higher speed on thehigh way than travelling in a local road segment with a speed limit of35 miles per hour. As a result, the ADV can invoke the polynomial-fitbased reference line smoothing component 403 to smooth a reference linegenerated for a road segment of the high way.

As used herein, in an embodiment, a reference line can be created in aform of a topographic map for each possible route determined by therouting module 307 from a starting point to a destination point.

When the ADV detects that it gets off the high way and starts to travelon a road segment with a speed limit that is lower than a predeterminedvalue (e.g., 45 miles/hour), the ADV can switch back to the genericreference line smoothing component 401 for reference line smoothing. Thepolynomial-fit based reference line smoothing component 403 canimplement a process for using a polynomial curve to generate a referenceline that is smoother and more precise.

FIG. 5 further illustrates a polynomial-fit based reference linesmoothing component, in accordance with an embodiment. In oneembodiment, a reference line smoothing process can be implemented by thepolynomial-fit based reference line smoothing component 403. Thesmoothing process can include a number of operations configured togenerate a reference line, and can provide a more comfortable drivingexperience in a high-speed environment.

As shown in FIG. 5, a raw reference line in a global coordinate system501 can be generated by the routing module 307 when the ADV detects thatit has entered a high-speed road segment. The raw reference line 501 caninclude a series of discrete points forming a line that connects astarting location to a destination location for a planning phase (e.g.,30 seconds). In an embodiment, a planning phase can be performed in anumber of planning cycles in a time interval of 100 milliseconds (ms).The series of points can be generated based on routing results of theADV and a high definition map. In one embodiment, the routing resultscan include various possible routes from the starting location to thedestination location. Each point on the raw reference line represents aposition of the ADV in the global coordinate system, and can thereforebe referenced by a longitude value and a latitude value.

As further shown in FIG. 5, the raw reference 501 can be truncated tobecome a truncated raw reference line 503. The truncating can be basedon a predetermined formula. In one example, the truncated raw referenceline can have a length computed using the formula “current speed*12seconds+30 meters”. The number of 12 seconds and 30 meters are valuedbased on historical data, and can be changed to another set of numbers.

The truncated reference line 503 can be converted to a series ofdiscrete points in a local coordinate system to create a truncated rawreference line 505 in the local coordinate system, where the x axis isthe heading of the ADV at the beginning of the planning phase. In thelocal coordinate system, there would be a set of coordinates (e.g., ahorizontal coordinate X, and a vertical coordinate Y) for each discretepoint on the truncated reference line. A polynomial curve 507 that bestfits the series of discrete points can be identified, and can be used tocalculate a new vertical coordinate 509 for each horizontal coordinate.Each newly calculated vertical coordinate and its correspondinghorizontal coordinate correspond to a point on a new smoothed referenceline that is represented by the polynomial curve. Each point on thesmoothed reference line can be converted back to a correspondingposition 511 in the global coordinate system.

From the polynomial curve and the points transferred back from the localcoordinate system, a number of parameters can be determined for eachpoint, for use by the ADV in following the newly generated referenceline, including a position, a heading, a kappa and a dkappa.

In an embodiment, the position of each point can be represented by alongitude value and a latitude value. The heading for each point can bederived from the fitted polynomial curve, for example, by using theformula y=atan 2(3*C₃*new_(x)*new_x+2*C₂*new_x+C₂,1)−target_heading+new_heading). In the above formulas, the “new_x” isthe same as x, as the same x is provided to the best-fitting curve,which may generate a new y; “new_heading” is the heading of the firstpoint in the in the original global coordinate system; and“target_heading” is the heading of the first point in the relativecoordinate system, which can be 0 all the time.

In an embodiment, the kappa information can be derived from the fittedpolynomial curve, for example, by using the formula (2*C₂+6*C₃*new_x).The dkappa information can be derived from the fitted polynomial curve,for example, by using the formula (6*C₃).

In an embodiment, atan 2 (y, x) is a function that returns the arctangent of the two numbers x and y. The result is an angle expressed inradians. In the above example, the first parameter3*C₃*new_(x)*new_x+2*C₂*new_x+C₂ of atan 2 is the first order derivativeof the best-fitting curve. The formula used to calculate the kappainformation of each point is the second order derivative of the bestfitting curve. The formula used to calculate the dkappa information ofeach point is the third order derivative of the best fitting curve. Inan embodiment, a kappa of a point can be the curvature, and the dkappaof that point can the rate of curvature (dkappa/dt).

Since the polynomial curve is smooth, its derivatives of differentorders are also smooth. A smoothed reference line as such would providea smoother and more comfortable driving experience in a high-speedenvironment, where higher requirements for curvatures are needed tomaintain a particular acceleration. The magnitude of the centripetalacceleration can be expressed by V²/R. In the above expression, V is thespeed of the ADV, and R is the radius of the curvature at a particularpoint.

FIGS. 6A-6E graphically illustrates a polynomial-fit based referenceline smoothing method in accordance with an embodiment. In FIG. 6A, araw reference line is generated by a routing module in an ADV. The rawreference line includes a series of discrete points in a globalcoordinate system, wherein the discrete points forms a line thatconnects a starting location to a destination location during a planningphase. In FIG. 6B, the raw reference line is truncated based on apredetermined formula to create a truncated raw reference line 515,which can include a series of discrete points 505-513. Each point can bereferenced by a longitude value and a latitude value. In FIG. 6C, theseries of discrete points 505-513 can be converted to a local coordinatesystem, where the x axis can be the heading of the ADV at the beginningof the planning phase or at the moment of the coordinate system change.FIG. 6D illustrates a polynomial curve that best fits the series ofdiscrete points 505-513 in the local coordinate system. As shown in FIG.6D, the best-fitting polynomial curve can be in the format of=C₃*X³+C₂*X²+C₁*X¹+C₀*X⁰. FIG. 6E shows a series of new points 515-523in the global coordinate system. Each of the series of new points515-523 can correspond to a point on the best-fitting polynomial curvein the local coordinate system. Using the best-fitting polynomial curve,the routing module can calculate a heading direction, a kappa, anddkappa for each of the discrete points on the best-fitting curve. Theseries of discrete points with the calculated information wouldrepresent a smooth reference line in a high-speed environment.

FIG. 7 is a flow diagram illustrating an example process of smoothing areference line in accordance with an embodiment. Process 700 may beperformed by processing logic that may include hardware (e.g.,circuitry, dedicated logic, programmable logic, a processor, aprocessing device, a central processing unit (CPU), a system-on-chip(SoC), etc.), software (e.g., instructions running/executing on aprocessing device), firmware (e.g., microcode), or a combinationthereof. In some embodiments, process 700 may be performed by one ormore of perception module 302, planning module 305, and routing module307 illustrated in FIG. 3A and FIG. 3B.

Referring to FIG. 7, in operation 701, a reference line generated for anautonomous driving vehicle is transferred from a global coordinatesystem to a local coordinate system. The reference line can be part of araw reference line generated by a routing module of an ADV during aplanning phase based on a high definition map and planning information.The raw reference line can include a series of discrete points, witheach point representing an expected position at a point of time in theplanning phase and referenced by a longitude value and a latitude value.By transferring the reference line from the global coordinate system tothe local coordinate system, the routing module of the ADV converts theposition values of each discrete point from a global coordinate format(e.g., a longitude value and a latitude value) to a local coordinateformat (e.g., a vertical coordinate and a horizontal coordinate). Whenperforming the conversion, the routing module uses the heading of theADV as the x axis.

In operation 703, a polynomial curve that best fits or matches thediscrete points on the truncated reference line can be used to determinea new vertical coordinate in the local coordinate system for thehorizontal coordinate of each of the series of points on the truncatedreference line. In an embodiment, regression and cross-validationtechniques can be used to find the best-fitting curve, which can be athird-degree polynomial function.

In operation 705, a series of points represented by new verticalcoordinates and the corresponding horizontal coordinates can beconverted back to the global coordinate system.

In operation 707, a heading, a kappa, and dkappa are determined for eachof the transferred points using the best-fitting polynomial curve. Theseries of points converted back to the global coordinate system and theabove-determined information for each point can provide sufficientinformation for an ADV to provide a smoother and more comfortabledriving experience in a high-speed environment that has a highercurvature requirements.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this disclosure. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 8 a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, the routing module 307 of FIG. 3A. System1500 can include many different components. These components can beimplemented as integrated circuits (ICs), portions thereof, discreteelectronic devices, or other modules adapted to a circuit board such asa motherboard or add-in card of the computer system, or as componentsotherwise incorporated within a chassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows® operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include 10 devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional IO device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other 10 devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, planning module 305, and control module306, and routing module 307. Processing module/unit/logic 1528 may alsoreside, completely or at least partially, within memory 1503 and/orwithin processor 1501 during execution thereof by data processing system1500, memory 1503 and processor 1501 also constitutingmachine-accessible storage media. Processing module/unit/logic 1528 mayfurther be transmitted or received over a network via network interfacedevice 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that includes hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method for operating anautonomous driving vehicle (ADV), the method comprising: transferring areference line generated for the ADV from a global coordinate system toa local coordinate system, the reference line including a plurality ofdiscrete points; determining, using a polynomial curve that best fitsthe plurality of discrete points, a new vertical coordinate for ahorizontal coordinate of each point on the reference line; transferringpoints represented by each new vertical coordinate and its correspondinghorizontal coordinate back to the global coordinate system; determining,using the polynomial curve, a heading direction, a kappa, and a dkappafor each transferred point; and controlling the ADV to navigate based onthe reference line having the transferred points in the globalcoordinate system.
 2. The method of claim 1, wherein the transferredpoints and the heading direction, the kappa and the dkappa for eachpoint are used to create a new reference line for the ADV.
 3. The methodof claim 1, wherein the reference line is part of a raw reference linegenerated for a planning phase, wherein the reference line is created bytruncating the raw reference line based on a set of predeterminedcriteria.
 4. The method of claim 3, wherein each point on the referenceline represents a position that the ADV is to be at a particular pointof time in the planning phase.
 5. The method of claim 1, wherein thepolynomial curve is in a format of Y=C₃*X³+C₂*X²+C₁*X¹+C₀*X⁰.
 6. Themethod of claim 1, wherein the ADV travels at a speed exceeding apredetermined value.
 7. The method of claim 6, wherein the ADV is toswitch to another process implementing a different reference linesmoothing method when the ADV travels at a speed at or below thepredetermined value.
 8. A non-transitory machine-readable medium havinginstructions stored therein, which when executed by a processor, causethe processor to perform operations, the operations comprising:transferring a reference line generated for an autonomous drivingvehicle (ADV), from a global coordinate system to a local coordinatesystem, the reference line including a plurality of discrete points;determining, using a polynomial curve that best matches the plurality ofdiscrete points, a new vertical coordinate for a horizontal coordinateof each point on the reference line; transferring points represented byeach new vertical coordinate and its corresponding horizontal coordinateback to the global coordinate system; determining, using the polynomialcurve, a heading direction, a kappa, and a dkappa for each transferredpoint; and controlling the ADV to navigate based on the reference linehaving the transferred points in the global coordinate system.
 9. Themachine-readable medium of claim 8, wherein the transferred points andthe heading direction, the kappa and the dkappa for each point are usedto create a new reference line for the ADV.
 10. The machine-readablemedium of claim 8, wherein the reference line is part of a raw referenceline generated for a planning phase, wherein the reference line iscreated by truncating the raw reference line based on a set ofpredetermined criteria.
 11. The machine-readable medium of claim 10,wherein each point on the reference line represents a position that theADV is to be at a particular point of time in the planning phase. 12.The machine-readable medium of claim 8, wherein the polynomial curve isin a format of Y=C₃*X³+C₂*X²+C₁*X¹+C₀*X⁰.
 13. The machine-readablemedium of claim 8, wherein the ADV travels at a speed exceeding apredetermined value.
 14. The non-transitory machine-readable medium ofclaim 13, wherein the ADV is to switch to another process implementing adifferent reference line smoothing method when the ADV travels at aspeed at or below the predetermined value.
 15. A data processing system,comprising: a processor; and a memory coupled to the processor to storeinstructions, which when executed by the processor, causing theprocessor to perform operations, the operations comprising: transferringa reference line generated for an autonomous driving vehicle (ADV) froma global coordinate system to a local coordinate system, the referenceline including a plurality of discrete points, determining, using apolynomial curve that best matches the plurality of discrete points, anew vertical coordinate for a horizontal coordinate of each point on thereference line, transferring points represented by each new verticalcoordinate and its corresponding horizontal coordinate back to theglobal coordinate system, determining, using the polynomial curve, aheading direction, a kappa, and a dkappa for each transferred point; andcontrolling the ADV to navigate based on the reference line having thetransferred points in the global coordinate system.
 16. The system ofclaim 15, wherein the transferred points and the heading direction, thekappa and the dkappa for each point are used to create a new referenceline for the ADV.
 17. The system of claim 15, wherein the reference lineis part of a raw reference line generated for a planning phase, whereinthe reference line is created by truncating the raw reference line basedon a set of predetermined criteria.
 18. The system of claim 17, whereineach point on the reference line represents a position that the ADV isto be at a particular point of time in the planning phase.
 19. Thesystem of claim 15, wherein the polynomial curve is in a format ofY=C₃*X³+C₂*X²+C₁*X¹+C₀*X⁰.
 20. The system of claim 15, wherein the ADVtravels at a speed exceeding a predetermined value, and wherein the ADVis to switch to another process implementing a different reference linesmoothing method when the ADV travels at a speed at or below thepredetermined value.